Table 3.

Ranked normalized scores for each machine learning algorithm by metric (average over datasets for the validation dataset). AdaBoost (ADA); Bernoulli Naive Bayes (BNB); Random forest (RF); support vector classification (SVC); Deep Neural Networks (DNN).

Algorithm	ACC	AUC	Cohen’s Kappa	F1-Score	MCC	Precision	Recall	Mean	Rank
RF	0.90	0.84	0.75	0.58	0.75	0.59	0.62	0.72	1
DNN 2	0.90	0.73	0.74	0.57	0.75	0.65	0.55	0.70	2
SVC	0.91	0.81	0.73	0.55	0.74	0.61	0.55	0.70	3
DNN 5	0.90	0.73	0.74	0.57	0.75	0.65	0.55	0.70	4
DNN 4	0.90	0.73	0.74	0.57	0.75	0.65	0.55	0.70	5
DNN 3	0.90	0.73	0.74	0.57	0.75	0.63	0.55	0.70	6
BNB	0.84	0.80	0.71	0.54	0.72	0.51	0.64	0.68	7
ADA	0.90	0.81	0.71	0.52	0.72	0.57	0.51	0.68	8